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The Quick and Easy Way to Analyze Numpy Arrays

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The quickest and easiest way to analyze NumPy arrays is by using the numpy.array() method. This method allows you to quickly and easily analyze the values contained in a numpy array. This method can also be used to find the sum, mean, standard deviation, max, min, and other useful analysis of the value contained within a numpy array. Sum You can find the sum of Numpy arrays using the np.sum() function.  For example:  import numpy as np  a = np.array([1,2,3,4,5])  b = np.array([6,7,8,9,10])  result = np.sum([a,b])  print(result)  # Output will be 55 Mean You can find the mean of a Numpy array using the np.mean() function. This function takes in an array as an argument and returns the mean of all the values in the array.  For example, the mean of a Numpy array of [1,2,3,4,5] would be  result = np.mean([1,2,3,4,5])  print(result)  #Output: 3.0 Standard Deviation To find the standard deviation of a Numpy array, you can use the NumPy std() function. This function takes in an array as a par
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Python Delete Duplicates in List Faster Way

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Removing duplicates in List simplified using SET method. It's a simple method. Just you need SET and Print to remove duplicates. Removing duplicates is common in Data science projects. What is list A list is a collection of elements. The elements can be duplicates or non-duplicates. Today's task is to remove duplicate elements in the List. Faster way to remove list duplicates Create a List Use SET Print the result List with duplicates my_list = ['The', 'unanimous', 'Declaration', 'of', 'the', 'thirteen','united', 'States', 'of', 'America,', 'When', 'in', 'the', 'Course', 'of', 'human'] Apply set method >>> non_dupes = set(my_list) Print Final list >>> print(non_dupes) Here, if you observe, there are no duplicates. The duplicates are now removed. It displays only non-duplicate values.  Here 'the' is a duplicate value. That'
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